How I Used Reddit + AI to Research Our Biggest Event in 10 Minutes
As a creative leader, I have a confession: I want research to inform everything my team does.
Not because I fetishize data. Not because I think spreadsheets make better creative decisions than humans. But because research shifts conversations from subjective opinions to objective problem-solving—and that makes everyone's job easier, from the junior designer defending their concept to the CMO approving the budget.
The problem? Traditional research methods are expensive and slow. And for smaller projects—seasonal campaigns, lead gen initiatives, content magnets—they're often completely off the table.
Until recently, anyway.
The Traditional Research Trap
Let me paint you a picture of how market research typically works:
Secondary Research: You purchase reports from providers like Mintel, Nielsen, or Forrester. These give you broad industry insights but rarely address your specific questions. Cost: $2,000–$10,000+ per report. Timeline: Immediate access, but the data is often 6-12 months old.
Primary Research Platforms: You use services like UserResearch.com, UserTesting, or Respondent.io to recruit participants and conduct studies. Cost: $3,000–$15,000+ depending on sample size and complexity. Timeline: 2-4 weeks from study design to final report.
Traditional Focus Groups: You partner with research firms to run qualitative studies. Cost: $8,000–$25,000+ for professional moderation and analysis. Timeline: 4-8 weeks.
Consulting Firms: You hire agencies like McKinsey, BCG, or specialized boutique firms. Cost: $50,000–$500,000+ for comprehensive research engagements. Timeline: 8-16+ weeks.
Don't get me wrong—these methods produce incredibly valuable insights. For major initiatives like rebrands, new product development, market entry strategies, or repositioning efforts, this depth of research isn't just valuable—it's essential.
But what about everything else?
What about the seasonal campaign that needs to ship in 3 weeks? The event presence you're planning for next quarter? The content magnet you're testing to improve lead quality? The messaging refresh for a single product vertical?
For these projects, traditional research is either:
Too expensive to justify
Too slow to be actionable
Both
So we make decisions based on intuition, past experience, and whoever argues most convincingly in the meeting. Sometimes it works out. Often it doesn't.
A Real Example: Preparing for SuiteWorld
This challenge hit home for me recently as I was planning Celigo's presence at SuiteWorld—our industry's biggest event and our most significant investment in terms of both spend and lead generation potential.
We've exhibited at SuiteWorld for years. We have some research from past events: post-event surveys, anecdotal feedback from sales conversations, comments our team collected at the booth. But this research is:
Fragmented across multiple systems (Google Drive, Asana, Slack, email)
Biased toward positive feedback (people are polite in person)
Incomplete (we only hear from people who talk to us)
Time-consuming to synthesize (requires reading through dozens of documents and threads)
Meanwhile, our target audience—NetSuite administrators, integration specialists, IT leaders—are actively discussing both SuiteWorld and Celigo on Reddit. Honest, unfiltered, recent conversations happening right now.
The traditional approach would be to either:
Pay $8,000–$15,000 for a research firm to conduct interviews with 10-15 attendees
Spend 20-30 hours manually reading through Reddit threads and taking notes
Skip the research entirely and rely on last year's approach
Instead, I spent 10 minutes and $0 using a combination of two tools: Apify (for data collection) and NotebookLM (for AI analysis).
The scraper ran for about 3-5 minutes and collected honest, unfiltered conversations from multiple subreddits where our target audience hangs out: r/Netsuite, r/integrations, r/ERP, and others.
If you prefer, you can just copy and paste this JSON code to automatically configure all fields the way I did for this demo example:
{
"debugMode": false,
"ignoreStartUrls": false,
"includeNSFW": true,
"maxComments": 2000,
"maxCommunitiesCount": 500,
"maxItems": 2000,
"maxPostCount": 2000,
"maxUserCount": 500,
"proxy": {
"useApifyProxy": true,
"apifyProxyGroups": [
"RESIDENTIAL"
]
},
"scrollTimeout": 360,
"searchComments": true,
"searchCommunities": false,
"searchPosts": true,
"searchUsers": false,
"searches": [
"\"Celigo\" AND \"SuiteWorld\""
],
"skipComments": false,
"skipCommunity": false,
"skipUserPosts": false,
"sort": "new"
}
Then hit Save.
Step 2: Exporting the Data (1 minute)
I exported the results as JSON, selecting only the "body" field (the actual comment text). This gives me the meat of the conversations without all the metadata like usernames, timestamps, and vote counts—which aren't relevant for brand sentiment analysis.
Then I renamed the file from .json to .txt so NotebookLM can ingest it.
Step 3: Upload to NotebookLM (30 seconds)
I created a new notebook in Google's NotebookLM and uploaded the renamed text file.
NotebookLM immediately began analyzing the content, creating a searchable knowledge base from 1,000+ real conversations about Celigo and SuiteWorld.
Step 4: Querying for Insights (5 minutes)
This is where the magic happens. Using NotebookLM's conversational interface, I asked a series of strategic questions:
Brand Perception:
"What do people most like about Celigo's presence at SuiteWorld?"
"What complaints or criticisms do people have about Celigo at SuiteWorld?"
"How do people describe Celigo compared to competitors at the event?"
Event Experience:
"What are the top things people like about SuiteWorld?"
"What are people's biggest frustrations with SuiteWorld?"
"What types of sessions or activities get the most positive mentions?"
Our VIP Event:
"What do people say about Celigo's VIP event at SuiteWorld?"
"What makes an event memorable or valuable at a conference like this?"
Competitive Intelligence:
"Which other vendors get mentioned in the same conversations as Celigo?"
"What booth experiences do people describe as particularly impressive or disappointing?"
Lead Quality:
"What problems are NetSuite administrators trying to solve when they visit SuiteWorld?"
"What makes someone likely to engage with a vendor at an event like this?"
NotebookLM provided detailed, source-backed answers to each question, pulling specific examples and quotes from the corpus of Reddit conversations.
Here's the crucial part: NotebookLM includes citations directly in its responses—little numbered references that link back to the specific Reddit comments or posts where each insight originated. This means I can:
Validate surprising insights by reading the full context of any counterintuitive finding
Dig deeper into patterns by exploring related comments from the same threads
Pull direct quotes for creative briefs and stakeholder presentations (anonymized, of course)
Build bulletproof recommendations backed by specific, verifiable source material
When a stakeholder questions an insight or pushes back on a recommendation, I can literally click through to the exact Reddit conversation that informed that decision. This transforms the creative brief from "here's what I think" to "here's what your target audience is actually saying."
What I Learned (That I Couldn't Have Known Otherwise)
The insights were immediate and actionable:
Surprising Discovery #1: People loved our swag from previous years, but specifically mentioned wanting "functional items they'd actually use" rather than "more conference junk." This directly informed our swag strategy—and because NotebookLM cited the exact comments, I could click through to see the full conversations where people listed specific examples of swag they kept versus swag they threw away.
Surprising Discovery #2: Attendees consistently praised vendors who offered "quick, technical demos" that "respected their time" versus generic sales pitches. This shaped our booth staffing and training approach. The citations let me trace this insight back to 15+ different comments across multiple threads—a clear pattern, not just one person's opinion.
Surprising Discovery #3: Our VIP event had strong positive sentiment, but people specifically valued the "ability to have real conversations with Celigo engineers" more than the venue or food. This changed our priorities for this year's event format. I could click directly to the Reddit discussions where attendees compared different vendors' hospitality events.
Surprising Discovery #4: Multiple conversations revealed that people were frustrated by booths that "made you scan your badge before you could ask questions." We ensured our booth design avoided this pain point. The citations made it easy to find and read the original complaints in context.
Could I have guessed some of these insights through experience and intuition? Maybe. But I wouldn't have known whichinsights to prioritize, and I certainly couldn't have walked into stakeholder meetings with direct quotes from our target audience backing up every recommendation—complete with citations I could show on screen if anyone doubted the research.
The Result: A Data-Backed Creative Brief in Under an Hour
Armed with these insights—and the citations to back them up—I created a comprehensive creative brief for our SuiteWorld presence that included:
Booth Design Requirements: Emphasizing open, accessible layouts with clear value propositions visible from the aisle
Staffing Strategy: Ensuring technical team members were present for substantive conversations, not just sales reps
Swag Direction: Focusing on 3-4 high-quality, functional items rather than variety for variety's sake
Demo Content: Creating 3-minute technical demos addressing specific pain points identified in the research
VIP Event Format: Doubling down on engineer accessibility rather than production value
Each recommendation in the brief included supporting evidence with citations back to the original Reddit discussions. When the VP of Marketing questioned whether we really needed to staff the booth with engineers (expensive!), I pulled up three specific Reddit threads where attendees praised technical conversations and criticized "booth bunnies who couldn't answer basic questions."
Stakeholder review time? Cut in half. Because instead of debating opinions, we were validating against customer voices.
Total investment: ~30 minutes of my time, $0 in research costs.
Compare that to:
UserResearch.com study: $5,000–$8,000, 2-3 weeks
Focus groups: $10,000–$15,000, 4-6 weeks
Research firm: $15,000–$25,000, 6-8 weeks
When This Approach Works (And When It Doesn't)
Let me be crystal clear: This is not a replacement for rigorous research on major initiatives.
Don't use this approach for:
Complete rebrands
New product development
Market entry strategies
Pricing research
Quantitative segmentation studies
Legal/regulatory compliance research
Anything where you need statistically significant sample sizes
Do use this approach for:
Event planning and presence
Seasonal campaign messaging
Content magnet themes and angles
Competitive positioning for specific campaigns
Social listening and brand sentiment
Supplementary research for larger initiatives
Quick validation of assumptions before investing in formal research
Lead gen campaign targeting and messaging
Think of it as research triage—a fast, cost-effective way to make smaller projects data-informed while saving your big research budget for initiatives that truly need professional depth.
Why This Matters for Creative Leaders
Here's what excites me most about this approach: It democratizes research for creative teams.
For years, research has been gatekept by budget and timeline constraints. Junior designers couldn't test their assumptions. Mid-level managers couldn't build data-backed cases for their recommendations. Creative leaders had to fight for research budget and then wait weeks for results.
Now? Anyone on your team can:
Validate a concept direction in minutes
Build evidence-based recommendations for stakeholders
Identify emerging themes in customer conversations
Benchmark competitor presence and messaging
Ground creative decisions in real customer language
Navigate stakeholder reviews with citable sources (not just "I think" but "here's what customers said")
And because NotebookLM provides citations for every insight, junior team members can present recommendations with the same confidence as a seasoned research analyst. Click, there's the source. Click, there's another example. Click, here are five more supporting comments.
This shifts the entire dynamic of creative development from "I think" to "I found." And that changes everything—from the quality of our work to how confidently we present it.
Beyond Reddit: Where Else This Works
The same methodology works for other social platforms where your audience has honest conversations:
Twitter/X: Use tools like Apify's Twitter Scraper or Tweepy (for technical users) to analyze brand mentions, competitor discussions, or industry conversations.
TikTok: Scrape comments and captions from relevant hashtags to understand emerging trends, language patterns, and sentiment.
Facebook Groups: Analyze discussions in industry-specific groups (with appropriate permissions and respect for community guidelines).
YouTube Comments: Gather feedback and sentiment from competitor product demos, tutorial videos, or industry thought leaders.
LinkedIn: Monitor discussions in relevant industry groups and comment sections.
The key is finding where your target audience has unfiltered conversations—not where they're being polite to your sales team.
The Bigger Picture: Research-Informed Creative Culture
In a perfect world, research would inform every decision my team makes. It leads to:
Better outcomes (we solve actual problems, not imagined ones)
Faster approval (data beats opinions)
More confident teams (designers can defend their work with evidence)
Less rework (we get things right the first time)
But until recently, this has been aspirational—especially for smaller projects without research budgets.
Now, with tools like this, we're getting closer to that ideal. Not by replacing professional research (which remains essential for major initiatives), but by making data-informed decisions accessible for everything else.
Try It Yourself
If you want to test this approach for your next project, here's my recommended starting process:
Identify your research questions (3-5 specific questions you need answered)
Choose your data source (Reddit, Twitter, YouTube comments, etc.)
Set up Apify scraper (use their free trial to test)
Export and upload to NotebookLM (rename .json to .txt)
Query strategically (ask specific, actionable questions)
Follow the citations (click through to validate insights and gather supporting examples)
Synthesize into briefs (translate insights into creative direction with citable sources)
Total time investment for your first attempt: ~20-30 minutes.
Total cost: $0 (using free trials).
The first time I did this, I was skeptical. It felt too easy—like there had to be a catch.
But the insights were legitimate. The sources were verifiable (I could click through to every single citation). And most importantly, the creative work improved because it was grounded in real customer language and actual pain points rather than assumptions.
Three months later, I use this approach weekly for everything from campaign messaging to event planning to content strategy. It's become part of how we work.
I'm currently seeking Director/VP-level creative leadership roles at established tech/SaaS companies. My background includes:
Brand Transformation: Led award-winning rebrand at Celigo (GDUSA, Gold ADDY recognition) that saved $500K+ on a single project
Creative Operations: Built systems that increased team output 238% while maintaining quality
Strategic Innovation: Developed AI-powered tools and data-informed processes that connect creative excellence to measurable business impact
View my portfolio or connect with me on LinkedIn if you'd like to chat about creative leadership, operational excellence, or how to build more research-informed creative teams.
